OpenRaiser/Intern-Atlas huggingface dataset:
Cooperative Learning with Visual Attributes
Venue: arXiv cs.CV 2017
Year: 2017

Learning paradigms involving varying levels of supervision have received a lot of interest within the computer vision and machine learning communities. The supervisory information is typically considered to come from a human supervisor -- a "teacher" figure. In this paper, we consider an alternate source of supervision -- a "peer" -- i.e. a different machine. We introduce cooperative learning, where two agents trying to learn the same visual concepts, but in potentially different environments using different sources of data (sensors), communicate their current knowledge of these concepts to each other. Given the distinct sources of data in both agents, the mode of communication between the two agents is not obvious. We propose the use of visual attributes -- semantic mid-level visual properties such as furry, wooden, etc.-- as the mode of communication between the agents. Our experiments in three domains -- objects, scenes, and animals -- demonstrate that our proposed cooperative learning approach improves the performance of both agents as compared to their performance if they were to learn in isolation. Our approach is particularly applicable in scenarios where privacy, security and/or bandwidth constraints restrict the amount and type of information the two agents can exchange.
Short Codes with Mismatched Channel State Information: A Case Study
Venue: arXiv cs.IT 2017
Year: 2017

The rising interest in applications requiring the transmission of small amounts of data has recently lead to the development of accurate performance bounds and of powerful channel codes for the transmission of short-data packets over the AWGN channel. Much less is known about the interaction between error control coding and channel estimation at short blocks when transmitting over channels with states (e.g., fading channels, phase-noise channels, etc...) for the setup where no a priori channel state information (CSI) is available at the transmitter and the receiver. In this paper, we use the mismatched-decoding framework to characterize the fundamental tradeoff occurring in the transmission of short data packet over an AWGN channel with unknown gain that stays constant over the packet. Our analysis for this simplified setup aims at showing the potential of mismatched decoding as a tool to design and analyze transmission strategies for short blocks. We focus on a pragmatic approach where the transmission frame contains a codeword as well as a preamble that is used to estimate the channel (the codeword symbols are not used for channel estimation). Achievability and converse bounds on the block error probability achievable by this approach are provided and compared with simulation results for schemes employing short low-density parity-check codes. Our bounds turn out to predict accurately the optimal trade-off between the preamble length and the redundancy introduced by the channel code.
Strong Coordination of Signals and Actions over Noisy Channels
Venue: arXiv cs.IT 2017
Year: 2017

-We develop a random binning scheme for strong coordination in a network of two nodes separated by a noisy channel, in which the input and output signals have to be coordinated with the source and its reconstruction. In the case of non-causal encoding and decoding, we propose a joint source-channel coding scheme and develop inner and outer bounds for the strong coordination region. While the set of achievable target distributions is the same as for empirical coordination, we characterize the rate of common randomness required for strong coordination.
Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming
Venue: arXiv cs.NE 2017
Year: 2017

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population.
The Parameterized Complexity of the Equidomination Problem
Venue: arXiv cs.CC 2017
Year: 2017

A graph $G=(V,E)$ is called equidominating if there exists a value $t \in \mathbb{N}$ and a weight function $ω: V \rightarrow \mathbb{N}$ such that the total weight of a subset $D\subseteq V$ is equal to $t$ if and only if $D$ is a minimal dominating set. To decide whether or not a given graph is equidominating is referred to as the Equidomination problem. In this paper we show that two parameterized versions of the Equidomination problem are fixed-parameter tractable: the first parameterization considers the target value $t$ leading to the Target-$t$ Equidomination problem. The second parameterization allows only weights up to a value $k$, which yields the $k$-Equidomination problem. In addition, we characterize the graphs whose every induced subgraph is equidominating. We give a finite forbidden induced subgraph characterization and derive a fast recognition algorithm.
Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers
Venue: arXiv cs.CV 2017
Year: 2017

Picasso is a free open-source (Eclipse Public License) web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks. Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend). Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures. Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.
Kleisli, Parikh and Peleg Compositions and Liftings for Multirelations
Venue: arXiv cs.LO 2017
Year: 2017

Multirelations provide a semantic domain for computing systems that involve two dual kinds of nondeterminism. This paper presents relational formalisations of Kleisli, Parikh and Peleg compositions and liftings of multirelations. These liftings are similar to those that arise in the Kleisli category of the powerset monad. We show that Kleisli composition of multirelations is associative, but need not have units. Parikh composition may neither be associative nor have units, but yields a category on the subclass of up-closed multirelations. Finally, Peleg composition has units, but need not be associative; a category is obtained when multirelations are union-closed.
Cloudroid: A Cloud Framework for Transparent and QoS-aware Robotic Computation Outsourcing
Venue: arXiv cs.DC 2017
Year: 2017

Many robotic tasks require heavy computation, which can easily exceed the robot's onboard computer capability. A promising solution to address this challenge is outsourcing the computation to the cloud. However, exploiting the potential of cloud resources in robotic software is difficult, because it involves complex code modification and extensive (re)configuration procedures. Moreover, quality of service (QoS) such as timeliness, which is critical to robot's behavior, have to be considered. In this paper, we propose a transparent and QoS-aware software framework called Cloudroid for cloud robotic applications. This framework supports direct deployment of existing robotic software packages to the cloud, transparently transforming them into Internet-accessible cloud services. And with the automatically generated service stubs, robotic applications can outsource their computation to the cloud without any code modification. Furthermore, the robot and the cloud can cooperate to maintain the specific QoS property such as request response time, even in a highly dynamic and resource-competitive environment. We evaluated Cloudroid based on a group of typical robotic scenarios and a set of software packages widely adopted in real-world robot practices. Results show that robot's capability can be enhanced significantly without code modification and specific QoS objectives can be guaranteed. In certain tasks, the "cloud + robot" setup shows improved performance in orders of magnitude compared with the robot native setup.
Volumetric Super-Resolution of Multispectral Data
Venue: arXiv cs.CV 2017
Year: 2017

Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7 ETM+) provide low-spatial high-spectral resolution multispectral (MS) or high-spatial low-spectral resolution panchromatic (PAN) images, separately. In order to reconstruct a high-spatial/high-spectral resolution multispectral image volume, either the information in MS and PAN images are fused (i.e. pansharpening) or super-resolution reconstruction (SRR) is used with only MS images captured on different dates. Existing methods do not utilize temporal information of MS and high spatial resolution of PAN images together to improve the resolution. In this paper, we propose a multiframe SRR algorithm using pansharpened MS images, taking advantage of both temporal and spatial information available in multispectral imagery, in order to exceed spatial resolution of given PAN images. We first apply pansharpening to a set of multispectral images and their corresponding PAN images captured on different dates. Then, we use the pansharpened multispectral images as input to the proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The proposed SRR method is obtained by deriving the subband relations between multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images comparing our method to conventional techniques.
Hierarchical Temporal Representation in Linear Reservoir Computing
Venue: arXiv cs.LG 2017
Year: 2017

Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the state signals. The potentiality of our approach is assessed on the class of Multiple Superimposed Oscillator tasks. Furthermore, our investigation provides useful insights to open a discussion on the main aspects that characterize the deep learning framework in the temporal domain.
Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person's signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This reflects in a relatively poor performance, with verification errors around 7% in the best systems in the literature. To address both the difficulty of obtaining good features, as well as improve system performance, we propose learning the representations from signature images, in a Writer-Independent format, using Convolutional Neural Networks. In particular, we propose a novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user. Extensive experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-160, we obtained a large improvement in state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to 6.97% in the literature. We also verified that the features generalize beyond the GPDS dataset, surpassing the state-of-the-art performance in the other datasets, without requiring the representation to be fine-tuned to each particular dataset.
Minimizing Communication Overhead in Window-Based Parallel Complex Event Processing
Venue: arXiv cs.DC 2017
Year: 2017

Distributed Complex Event Processing has emerged as a well-established paradigm to detect situations of interest from basic sensor streams, building an operator graph between sensors and applications. In order to detect event patterns that correspond to situations of interest, each operator correlates events on its incoming streams according to a sliding window mechanism. To increase the throughput of an operator, different windows can be assigned to different operator instances---i.e., identical operator copies---which process them in parallel. This implies that events that are part of multiple overlapping windows are replicated to different operator instances. The communication overhead of replicating the events can be reduced by assigning overlapping windows to the same operator instance. However, this imposes a higher processing load on the single operator instance, possibly overloading it. In this paper, we address the trade-off between processing load and communication overhead when assigning overlapping windows to a single operator instance. Controlling the trade-off is challenging and cannot be solved with traditional reactive methods. To this end, we propose a model-based batch scheduling controller building on prediction. Evaluations show that our approach is able to significantly save bandwidth, while keeping a user-defined latency bound in the operator instances.
Time-frequency or time-scale representation fission and fusion rules
Venue: arXiv cs.SD 2017
Year: 2017

Time-frequency representations are important for the analysis of time series. We have developed an online time-series analysis system and equipped it to reliably handle re-alignment in the time-frequency plane. The system can deal with issues like invalid regions in time-frequency representations and discontinuities in data transmissions, making it suitable for on-line processing in real-world situations. In retrospect the whole problem can be considered to be a generalization of ideas present in overlap-and-add filtering, but then for time-frequency representations and including the calculation of non-causal features. Here we present our design for time-frequency representation fission and fusion rules. We present these rules in the context of two typical use cases, which facilitate understanding of the underlying choices.
Small cities face greater impact from automation
Venue: arXiv cs.CY 2017
Year: 2017

The city has proven to be the most successful form of human agglomeration and provides wide employment opportunities for its dwellers. As advances in robotics and artificial intelligence revive concerns about the impact of automation on jobs, a question looms: How will automation affect employment in cities? Here, we provide a comparative picture of the impact of automation across U.S. urban areas. Small cities will undertake greater adjustments, such as worker displacement and job content substitutions. We demonstrate that large cities exhibit increased occupational and skill specialization due to increased abundance of managerial and technical professions. These occupations are not easily automatable, and, thus, reduce the potential impact of automation in large cities. Our results pass several robustness checks including potential errors in the estimation of occupational automation and sub-sampling of occupations. Our study provides the first empirical law connecting two societal forces: urban agglomeration and automation's impact on employment.
Completeness Theorems for Pomset Languages and Concurrent Kleene Algebras
Venue: arXiv cs.FL 2017
Year: 2017

Pomsets constitute one of the most basic models of concurrency. A pomset is a generalisation of a word over an alphabet in that letters may be partially ordered. A term $t$ using the bi-Kleene operations $0,1, +, \cdot\, ,^*, \parallel, ^{(*)}$ defines a language $ \mathopen{[\![ } t \mathclose{]\!] } $ of pomsets in a natural way. We prove that every valid universal equality over pomset languages using these operations is a consequence of the equational theory of regular languages (in which parallel multiplication and iteration are undefined) plus that of the commutative-regular languages (in which sequential multiplication and iteration are undefined). We also show that the class of $\textit{rational}$ pomset languages (that is, those languages generated from singleton pomsets using the bi-Kleene operations) is closed under all Boolean operations. An $ \textit{ideal}$ of a pomset $p$ is a pomset using the letters of $p$, but having an ordering at least as strict as $p$. A bi-Kleene term $t$ thus defines the set $ \textbf{Id} (\mathopen{[\![ } t \mathclose{]\!] }) $ of ideals of pomsets in $ \mathopen{[\![ } t \mathclose{]\!] } $. We prove that if $t$ does not contain commutative iteration $^{(*)}$ (in our terminology, $t$ is bw-rational) then $\textbf{Id} (\mathopen{[\![ } t \mathclose{]\!] }) \cap \textbf{Pom}_{sp}$, where $ \textbf{Pom}_{sp}$ is the set of pomsets generated from singleton pomsets using sequential and parallel multiplication ($ \cdot$ and $ \parallel$) is defined by a bw-rational term, and if two such terms $t,t'$ define the same ideal language, then $t'=t$ is provable from the Kleene axioms for $0,1, +, \cdot\, ,^*$ plus the commutative idempotent semiring axioms for $0,1, +, \parallel$ plus the exchange law $ (u \parallel v)\cdot ( x \parallel y) \le v \cdot y \parallel u \cdot x $.
Motion-Compensated Autonomous Scanning for Tumour Localisation using Intraoperative Ultrasound
Venue: arXiv cs.RO 2017
Year: 2017

Intraoperative ultrasound facilitates localisation of tumour boundaries during minimally invasive procedures. Autonomous ultrasound scanning systems have been recently proposed to improve scanning accuracy and reduce surgeons' cognitive load. However, current methods mainly consider static scanning environments typically with the probe pressing against the tissue surface. In this work, a motion-compensated autonomous ultrasound scanning system using the da Vinci Research Kit (dVRK) is proposed. An optimal scanning trajectory is generated considering both the tissue surface shape and the ultrasound transducer dimensions. A robust vision-based approach is proposed to learn the underlying tissue motion characteristics. The learned motion model is then incorporated into the visual servoing framework. The proposed system has been validated with both phantom and ex vivo experiments using the ground truth motion data for comparison.
The Effect of Collective Attention on Controversial Debates on Social Media
Venue: arXiv cs.SI 2017
Year: 2017

We study the evolution of long-lived controversial debates as manifested on Twitter from 2011 to 2016. Specifically, we explore how the structure of interactions and content of discussion varies with the level of collective attention, as evidenced by the number of users discussing a topic. Spikes in the volume of users typically correspond to external events that increase the public attention on the topic -- as, for instance, discussions about `gun control' often erupt after a mass shooting. This work is the first to study the dynamic evolution of polarized online debates at such scale. By employing a wide array of network and content analysis measures, we find consistent evidence that increased collective attention is associated with increased network polarization and network concentration within each side of the debate; and overall more uniform lexicon usage across all users.
LoRa Backscatter: Enabling The Vision of Ubiquitous Connectivity
Venue: arXiv cs.NI 2017
Year: 2017

The vision of embedding connectivity into billions of everyday objects runs into the reality of existing communication technologies --- there is no existing wireless technology that can provide reliable and long-range communication at tens of microwatts of power as well as cost less than a dime. While backscatter is low-power and low-cost, it is known to be limited to short ranges. This paper overturns this conventional wisdom about backscatter and presents the first wide-area backscatter system. Our design can successfully backscatter from any location between an RF source and receiver, separated by 475 m, while being compatible with commodity LoRa hardware. Further, when our backscatter device is co-located with the RF source, the receiver can be as far as 2.8 km away. We deploy our system in a 4,800 $ft^{2}$ (446 $m^{2}$) house spread across three floors, a 13,024 $ft^{2}$ (1210 $m^{2}$) office area covering 41 rooms, as well as a one-acre (4046 $m^{2}$) vegetable farm and show that we can achieve reliable coverage, using only a single RF source and receiver. We also build a contact lens prototype as well as a flexible epidermal patch device attached to the human skin. We show that these devices can reliably backscatter data across a 3,328 $ft^{2}$ (309 $m^{2}$) room. Finally, we present a design sketch of a LoRa backscatter IC that shows that it costs less than a dime at scale and consumes only 9.25 $μ$W of power, which is more than 1000x lower power than LoRa radio chipsets.
Utility Maximizing Sequential Sensing Over a Finite Horizon
Venue: arXiv cs.SY 2017
Year: 2017

We consider the problem of optimally utilizing $N$ resources, each in an unknown binary state. The state of each resource can be inferred from state-dependent noisy measurements. Depending on its state, utilizing a resource results in either a reward or a penalty per unit time. The objective is a sequential strategy governing the decision of sensing and exploitation at each time to maximize the expected utility (i.e., total reward minus total penalty and sensing cost) over a finite horizon $L$. We formulate the problem as a Partially Observable Markov Decision Process (POMDP) and show that the optimal strategy is based on two time-varying thresholds for each resource and an optimal selection rule for which resource to sense. Since a full characterization of the optimal strategy is generally intractable, we develop a low-complexity policy that is shown by simulations to offer near optimal performance. This problem finds applications in opportunistic spectrum access, marketing strategies and other sequential resource allocation problems.
Target Type Identification for Entity-Bearing Queries
Venue: arXiv cs.IR 2017
Year: 2017

Identifying the target types of entity-bearing queries can help improve retrieval performance as well as the overall search experience. In this work, we address the problem of automatically detecting the target types of a query with respect to a type taxonomy. We propose a supervised learning approach with a rich variety of features. Using a purpose-built test collection, we show that our approach outperforms existing methods by a remarkable margin. This is an extended version of the article published with the same title in the Proceedings of SIGIR'17.